We refer our novel object detection framework, which explicitly models relationships between objects over the directed graph with their uncertainties, as Uncertainty-Aware Graph network for object DETection (UAGDet), which is illustrated in Fig. REF . We then experimentally validate our UAGDet on the large-scale Dataset for Object deTection in Aerial images (DOTA) [1]} with two different versions: DOTA-v1.0 and DOTA-v1.5, containing large numbers of small and uncertain objects with sufficient interaction existing among them. Therefore, we suppose our UAGDet is highly beneficial in such a challenging scenario. Experimentally, we use RoITrans [2]} as a backbone object detection network, and the results show that our UAGDet shows 2.32% and 3.82% performance improvements against the backbone RoITrans network on DOTA-v1.0 and -v1.5 datasets, respectively, with mean Average Precision (mAP) as an evaluation metric. Also, further analyses show that our graph construction method considering both semantic and spatial features connects related objects by edges, and also uncertainties are measured high on incorrect predictions which are corrected by refined features from graph representations.
